MétaCan
Menu
Retour à la cohorte
Enregistrement W2315208310 · doi:10.1097/01.sih.0000441538.76503.75

Board 273 - Program Innovations Abstract The Use of Hybrid Simulation to Teach Family Communication in Critical Care Fellows - A Novel Approach (Submission #516)

2013· article· en· W2315208310 sur OpenAlexaffabout
Tobias Witter, Janice Chisholm, J. J. Evans, Doug Ferkol, Stephen Beed, D. Bruce Holmes

Notice bibliographique

RevueSimulation in Healthcare The Journal of the Society for Simulation in Healthcare · 2013
Typearticle
Langueen
DomaineMedicine
ThématiquePalliative Care and End-of-Life Issues
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésConsistency (knowledge bases)FidelityCurriculumMedical educationResource (disambiguation)Patient safetyCommunication skillsMedicinePsychologyComputer sciencePedagogyHealth care

Résumé

récupéré en direct d'OpenAlex

Introduction/Background High fidelity simulation has been shown to be an effective tool in teaching various aspects of critical care practicd.1 This includes not only technical skills like inserting lines, performing intubations or chest tubes etc. but also some non-technical skills, in particular crisis resource management.2-6 Over the years, high fidelity simulation has become standard in our institution and we believe this provides a safe learning environment for our trainees and ultimately, improved patient safety. In the critical care environment, family interactions are a very important part of the daily routine and yet the skill of communicating with families has not been addressed in our simulated teaching. Given the deficiency, a new communication curriculum was developed that combines high fidelity simulation with simulated patients who act as family members. Methods In collaboration with communications skills experts from the Dalhousie Faculty of Medicine, Division of Medical Education, a scenario was designed which combined high fidelity simulation with family communication using simulated patients. In the high fidelity component, a critically ill patient was admitted to the ICU with a severe head injury and progressed to neurological death. To keep the fellow engaged in the high fidelity simulation, the fellows managed treatable complications (e.g. tension pneumothorax) but these did not alter the overall outcome. For the communication component, a family consisting of three family members was created using simulated patient actors.. During the interaction with the trainees, the "family" followed a script to make sure there was consistency in each meeting. Initially, they met the "family" for the first time, updated them on the current status of their loved one and informed them of the potential of a very poor outcome. Later, the fellow communicated to the family that their loved one had passed away. The fellows were assessed for their management of the critically ill patient in the ICU with regards to medical skills and crisis resource management skills. The family meeting was assessed using a communications checklist developed by the communication skills experts in consultation with ICU staff. The checklist was based on previously published work on breaking bad news in Palliative care and Oncology.7-12 It focuses on verbal and non-verbal communication, collecting and providing of information, the structure and planning of the meeting and empathy displayed during the meeting. Accordingly, the fellows received direct feedback from the "family" actors and an independent observer including comments on these topics. Results: Conclusion With this novel approach of combining high fidelity simulation with actors trained in giving trainees communication feedback, a realistic situation was created in which the fellow switched from patient care to compassionate family care and back. The scripted approach of the family meeting and the specifically developed standardized evaluation tool allowed us to compare different approaches trainees use for particular situations arising in the meeting. Since our family actors have a non-medical background but are very experienced and trained in giving feedback, many areas of improvement were highlighted that would have escaped the traditional checklist evaluation and led to a great acceptance of this novel approach by our trainees. References 1. Barsuk JH, McGaghie WC, Cohen ER, Balachandran JS, Wayne DB. Use of simulation-based mastery learning to improve the quality of central venous catheter placement in a medical intensive care unit. J Hosp Med. 2009;4(7):397-403. doi: 10.1002/jhm.468; 10.1002/jhm.468. 2. Britt RC, Novosel TJ, Britt LD, Sullivan M. The impact of central line simulation before the ICU experience. Am J Surg. 2009;197(4):533-536. doi: 10.1016/j.amjsurg.2008.11.016;10.1016/j.amjsurg.2008.11.016. 3. Cheruparambath V, Sampath S, Deshikar LN, Ismail HM, Bhuvana K. A low-cost reusable phantom for ultrasound-guided subclavian vein cannulation. Indian J Crit Care Med. 2012;16(3):163-165. doi: 10.4103/0972-5229.102097; 10.4103/0972-5229.102097. 4. Clapper T. Development of a hybrid simulation course to reduce central line infections. J Contin Educ Nurs. 2012;43(5):218-224. doi: 10.3928/00220124-20111101-06; 10.3928/00220124-20111101-06. 5. Figueroa MI, Sepanski R, Goldberg SP, Shah S. Improving teamwork, confidence, and collaboration among members of a pediatric cardiovascular intensive care unit multidisciplinary team using simulation-based team training. Pediatr Cardiol. 2013;34(3):612-619. doi: 10.1007/s00246-012-0506-2; 10.1007/s00246-012-0506-2. 6. Pastis NJ, Doelken P, Vanderbilt AA, Walker J, Schaefer JJ,3rd. Validation of simulated difficult bag-mask ventilation as a training and evaluation method for first-year internal medicine house staff. Simul Healthc. 2013;8(1):20-24. doi: 10.1097/SIH.0b013e318263341f; 10.1097/SIH.0b013e318263341f. 7. Buckman R. Breaking bad news: The S-P-I-K-E-S strategy. In: Community oncology. 2nd ed. ; 2005:138. 8. Davidson JE, Powers K, Hedayat KM, et al. Clinical practice guidelines for support of the family in the patient-centered intensive care unit: American college of critical care medicine task force 2004-2005. Crit Care Med. 2007;35(2):605-622. doi: 10.1097/01.CCM.0000254067.14607.EB. 9. Kaplan M. SPIKES: A framework for breaking bad news to patients with cancer. Clin J Oncol Nurs. 2010;14(4):514-516. doi: 10.1188/10.CJON.514-516; 10.1188/10.CJON.514-516. 10. Kramer BJ, Boelk AZ, Auer C. Family conflict at the end of life: Lessons learned in a model program for vulnerable older adults. J Palliat Med. 2006;9(3):791-801. doi: 10.1089/jpm.2006.9.791. 11. Platt F, Gordon G. Field guide to the difficult patient interview. 2nd ed. New York: Lippincott Williams & Wilkiens; 2004. 12. Silverman J, Kurtz S, Draper J. Skills for communication with patients. 2nd ed. Oxon, UK: Radcliffe Publishing; 2005. Disclosures Fresenius Kabi.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,203
Score d'incertitude au seuil0,679

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,2030,031

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,332
Tête enseignante GPT0,509
Écart entre enseignants0,177 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2013
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueSimulation in Healthcare The Journal of the Society for Simulation in HealthcareMême sujetPalliative Care and End-of-Life IssuesTravaux en français237 207